Deep learning for urban remote sensing

Nicolas Audebert, Alexandre Boulch, Hicham Randrianarivo, Bertrand Le Saux, Marin Ferecatu, Sebastien Lefevre, Renaud Marlet
2017 2017 Joint Urban Remote Sensing Event (JURSE)  
This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by
more » ... ng hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.
doi:10.1109/jurse.2017.7924536 dblp:conf/jurse/AudebertBRSFLM17 fatcat:4x55h56kifhmpgqoaqnhxckgu4